Computer Engineering | Robotics
Certain robot missions need to perform predictably in a physical environment that may only be poorly characterized in advance. We have previously developed an approach to establishing performance guarantees for behaviorbased controllers in a process-algebra framework. We extend that work here to include random variables, and we show how our prior results can be used to generate a Dynamic Bayesian Network for the coupled system of program and environment model. Verification is reduced to a filtering problem for this network. Finally, we present validation results that demonstrate the effectiveness of the verification of a multiple waypoint robot mission using this approach.
Lyons, Damian M.; Arkin, Ronald C.; Nirmal, Paramesh; Jiang, Shu; Liu, Tsung-Ming; and Deeb, J., "Getting it Right the First time: Robot Mission Guarantees in the Presence of Uncertainty" (2013). Faculty Publications. 36.